An Approach to Develop a Smart and Intelligent Wheelchair

Soma, Shridevi; Patil, Nandita; Salva, F; Jadhav, Vaishnavi

doi:10.1109/icccnt.2018.8494050

Cited by 7 publications

(1 citation statement)

References 4 publications

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“…When applied to AT, AIoT allows the conception of an array of disruptive solutions to address the disability issue. Some examples of such solutions are navigation systems for blind people, voice assistants for people with disabilities [ 8 ], the remote monitoring of health conditions [ 9 ], telemedicine and telehealth [ 10 ], communication systems based on sign language [ 11 ], auxiliary memory for people with cognitive disabilities, and a series of smart objects such as medicine dispensers, wheelchairs [ 12 ], exoskeletons [ 13 ], etc. These are some of the numerous applications of great value for those in need, quoting Mary Pat Radabaugh (the former director of IBM’s National Support Center for People with Disabilities in 1988) “For people without disabilities, technology makes life easier.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Freitas

Piai

Farias

et al. 2022

Sensors

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According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Freitas

Piai

Farias

et al. 2022

Sensors

View full text Add to dashboard Cite

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Design and Implementation of Voice Controlled Wheelchair using MATLAB

Karande,

Somani,

Zope

et al. 2022

ITM Web Conf.

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Most physically challenged people to rely on others for their motion and the number of such people is increasing day by day. Manual Wheelchairs are quite difficult to operate by physically challenged people as they require force for the motion. To overcome this drawback, conventional wheelchairs or powered wheelchairs are available in the markets. Even if they are meant to design for the comfort of users they are quite expensive, so not every physically challenged can afford them. To reduce the percentage of use of a manual wheelchair and to give physically challenged people a better experience and comfort in an affordable manner we have proposed this model. Our proposed model is based on neural networks and the backpropagation algorithm is used as a medium to train the Artificial neural networks which are trained by the user’s voice command so it uses voice recognition as a control mechanism. The proposed prototype is been trained with five voice commands such as (Forward, Backward, Left, Right, and Stop). These voice commands are successfully identified and converted to text. The following text is further written on target hardware wirelessly by using a Bluetooth module for the motion of DC Motors to control the directions of a wheelchair. The entire prototype is been designed on Matlab R2020a and Arduino UNO as target hardware. The main aim of our proposed work is to develop a smart wheelchair that overcomes the problems of existing systems and should be easy, impressionable, and convenient for physically challenged people.

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